# US 2021/0344801 A1 · Method and system for fraud clustering by content and biometrics analysis

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- Number: US 2021/0344801 A1
- Document ID: US20210344801A1
- Status: Published application
- Kind: A1
- Application number: 17/376,209
- Filed: 2021-07-15
- Published: 2021-11-04
- Earliest claimed: 2019-07-30
- Assignees: Nice Ltd.
- Applicants: Nice Ltd
- Inventors: Roman FRENKEL; Matan KERET; Roman SHTERNHARTS; Itay KALMAN HAREL; Galya Julya BAR; Yaara BAR; Michal DAISEY MOMIKA
- Web page: https://patentgrep.com/patent/US20210344801A1

## CPC

- H04M 3/5175: {Call or contact centers supervision arrangements}
- G06F 21/32: using biometric data, e.g. fingerprints, iris scans or voiceprints
- G06F 21/552: {involving long-term monitoring or reporting}
- G10L 15/22: Procedures used during a speech recognition process, e.g. man-machine dialogue
- G10L 15/26: Speech to text systems (G10L15/08 takes precedence)
- G10L 17/00: Speaker identification or verification techniques
- G10L 17/04: Training, enrolment or model building
- G10L 17/08: Use of distortion metrics or a particular distance between probe pattern and reference templates
- G10L 17/26: Recognition of special voice characteristics, e.g. for use in lie detectors; Recognition of animal voices
- H04M 3/5166: {in combination with interactive voice response systems or voice portals, e.g. as front-ends}
- H04M 3/5191: {interacting with the Internet}
- G06F 16/35: Clustering; Classification
- G06F 40/289: Phrasal analysis, e.g. finite state techniques or chunking
- G06F 40/35: Discourse or dialogue representation
- H04M 3/42221: {Conversation recording systems (at the subscriber's set H04M1/656)}
- H04M 2201/38: Displays
- H04M 2203/6027: Fraud preventions
- H04M 2203/6045: Identity confirmation
- H04M 2203/6054: Biometric subscriber identification
- H04M: TELEPHONIC COMMUNICATION (circuits for controlling other apparatus via a telephone cable and not involving telephone switching apparatus G08)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING

## Abstract

A computer-implemented method for proactive fraudster exposure in a customer service center according to content analysis and voice biometrics analysis, is provided herein. The computer-implemented method includes: (i) performing a first type analysis to cluster the call interactions into ranked clusters and storing the ranked clusters in a clusters database; (ii) performing a second type analysis on a predefined amount of the highest ranked clusters, into ranked clusters and storing the ranked clusters; the first type analysis is a content analysis and the second type analysis is a voice biometrics analysis, or vice versa; (iii) retrieving from the ranked clusters, a list of fraudsters; and (iv) transmitting the list of potential fraudsters to an application to display to a user said list of potential fraudsters via a display unit.

## Claims

### Claim 1

A computer-implemented method for proactive fraudster exposure in a customer service center according to content analysis and voice biometrics analysis of a call interaction, the computer-implemented method comprising:
(a) performing a first type analysis to cluster the call interactions in the interactions database into first type ranked clusters and storing the first type ranked clusters in a clusters database;
(b) performing a second type analysis on a predefined amount of highest ranked clusters of the first type ranked clusters, into second type ranked clusters and storing the second type ranked clusters in the clusters database;
wherein the first type analysis is a content analysis of a call interaction and the second type analysis is a voice biometrics analysis of the call interaction, or vice versa,
(c) retrieving from the second type ranked clusters in the clusters database, a list of potential fraudsters; and
(d) transmitting the list of potential fraudsters to an application to display to a user said list of potential fraudsters via a display unit, thus enabling said user to review said list of potential fraudsters and to add fraudsters from the list to a watchlist database.

### Claim 2 (depends on 1)

The computer-implemented method of claim 1, wherein before the retrieving from the second type ranked clusters in the clusters database, the computer-implemented method is further enabling a user to repeat operations (a) and (b).

### Claim 3 (depends on 1)

The computer-implemented method of claim 1, wherein the content analysis of the call interaction comprising:
performing Automatic Speech Recognition (ASR) for each call interaction to yield a textual transcript thereof;
operating a clustering algorithm over each textual transcript to cluster phrases in the textual transcript into clusters according to topics; and
identifying clusters with topics having predefined suspicious phrases and ranking the identified clusters.

### Claim 4 (depends on 3)

The computer-implemented method of claim 3, wherein the identifying is performed according to at least one of the following algorithms: graph theory, machine learning, statistics, linguistic, deep learning, hybrid, Term Frequency-Inverse Document Frequency (TF-IDF) and parallel word importance estimator.

### Claim 5 (depends on 3)

The computer-implemented method of claim 3, wherein, the content analysis of the call interaction further comprising:
representing a predefined amount of highest ranked phrases as nodes in a connected graph;
calculating phrase statistics to suggest to a user a connection between the phrases according to the graph; and
enabling a user to select a specific phrase in the graph for biometrics analysis of related call interactions.

### Claim 6 (depends on 1)

The computer-implemented method of claim 1, wherein the voice biometrics analysis of the call interaction is performed by a Proactive Fraud Exposure (PFE) engine, wherein the PFE is performing clustering.

### Claim 7 (depends on 6)

The computer-implemented method of claim 6, wherein the PFE engine is performing the clustering according to at least one of: K-means, K-centroids, Clustering Using REpresentatives (CURE), and Agglomerative Hierarchal Clustering (AHC) algorithms.

### Claim 8 (depends on 3)

The computer-implemented method of claim 3, wherein the content analysis of the call interaction further includes stemming to transform phrases into a predefined form.

### Claim 9 (depends on 1)

The computer-implemented method of claim 1, wherein before performing the first type analysis and the second type analysis:
(a) collecting call interactions from a database of recorded calls in a customer service channel,
wherein the collecting is based on predefined rules by a calls collection engine, and
(b) storing the collected call interactions in an interactions database.

### Claim 10

A computerized-system for proactive fraudster exposure in a customer service center according to content analysis and voice biometrics analysis of a call interaction, the computerized-system comprising:
a database of recorded calls;
an interactions database;
a clusters database;
a watchlist database;
a memory to store the database of recorded calls, the interactions database, the clusters database and the watchlist database;
a display unit;
and
a processor, said processor is configured to:
(a) perform a first type analysis to cluster the call interactions in the interactions database into first type ranked clusters and to store the first type ranked clusters in the clusters database; and
(b) perform a second type analysis on a predefined amount of highest ranked clusters, into second type ranked clusters and to store the second type ranked clusters in the clusters database;
wherein the first type analysis is a content analysis of a call interaction and the second type analysis is a voice biometrics analysis of the call interaction, or vice versa,
(c) retrieve from the second type ranked clusters in the clusters database, a list of potential fraudsters; and
(d) transmit the list of potential fraudsters to an application to display to a user said list of potential fraudsters via a display unit, thus, to enable said user to review said list of potential fraudsters and to add fraudsters from the list to the watchlist database.

### Claim 11 (depends on 10)

The computerized-system method of claim 10, wherein before the processor is retrieving from the second type ranked clusters in the clusters database, the processor is further configured to enable a user to repeat operations (a) and (b).

### Claim 12 (depends on 10)

The computerized-system of claim 10, wherein the content analysis of the call interaction comprising:
performing Automatic Speech Recognition (ASR) for each call interaction to yield a textual transcript thereof;
operating a clustering algorithm over each textual transcript to cluster phrases in the textual transcript into clusters according to topics; and
identifying clusters with topics having predefined suspicious phrases and ranking the identified clusters.

### Claim 13 (depends on 12)

The computerized-system of claim 12, wherein the identifying is performed according to at least one of the following algorithms: graph theory, machine learning, statistics, linguistic, deep learning, hybrid, Term Frequency-Inverse Document Frequency (TF-IDF) and parallel word importance estimator.

### Claim 14 (depends on 12)

The computerized-system of claim 12, wherein, the content analysis of the call interaction further comprising:
representing a predefined amount of highest ranked phrases as nodes in a connected graph;
calculating phrase statistics to suggest to a user a connection between the phrases according to the graph; and
enabling a user to select a specific phrase in the graph for biometrics analysis of related call interactions.

### Claim 15 (depends on 10)

The computerized-system of claim 10, wherein the voice biometrics analysis of the call interaction is performed by a Proactive Fraud Exposure (PFE) engine, wherein the PFE is performing clustering.

### Claim 16 (depends on 15)

The computerized-system of claim 15, wherein the PFE engine is performing the clustering according to at least one of: K-means, K-centroids, Clustering Using REpresentatives (CURE), and Agglomerative Hierarchal Clustering (AHC) algorithms.

### Claim 17 (depends on 12)

The computerized-system of claim 12, wherein the content analysis of the call interaction further includes stemming to transform phrases into a predefined form.

### Claim 18 (depends on 10)

The computerized-system of claim 10, wherein said processor is further configured before the performing of the first type analysis and the second type analysis to:
(a) collect call interactions from the database of recorded calls in a customer service channel,
wherein the collecting is based on predefined rules by a calls collection engine, and
(b) store the collected call interactions in the interactions database.